kopipasta 0.40.0

A CLI tool to generate prompts with project structure and file contents

kopipasta

A CLI tool for taking full, transparent control of your LLM context. No black boxes.

• An LLM told me that "kopi" means Coffee in some languages... and a Diffusion model then made this delicious soup.

The Philosophy: You Control the Context

Many AI coding assistants use Retrieval-Augmented Generation (RAG) to automatically find what they think is relevant context. This is a black box. When the LLM gives a bad answer, you can't debug it because you don't know what context it was actually given.

kopipasta is the opposite. I built it for myself on the principle of explicit context control. You are in the driver's seat. You decide exactly what files, functions, and snippets go into the prompt. This transparency is the key to getting reliable, debuggable results from an LLM.

It's a "smart copy" command for your project, not a magic wand.

How It Works

The workflow is dead simple:

1. Gather: Run kopipasta and point it at the files, directories, and URLs that matter for your task.
2. Select: The tool interactively helps you choose what to include. For large files, you can send just a snippet or even hand-pick individual functions.
3. Define: Write your instructions to the LLM in an interactive prompt directly in your terminal.
4. Paste: The final, comprehensive prompt is now on your clipboard, ready to be pasted into ChatGPT, Gemini, Claude, or your LLM of choice.
5. Apply: Inside the file selector, press p, paste the LLM's markdown response, and the tool will automatically patch your local files.

Installation

# Using pipx (recommended for CLI tools)
pipx install kopipasta

# Or using standard pip
pip install kopipasta

Usage

kopipasta has two main modes: creating prompts and applying patches.

Creating a Prompt

kopipasta [options] [files_or_directories_or_urls...]

Arguments:

• [files_or_directories_or_urls...]: One or more paths to files, directories, or web URLs to use as the starting point for your context.

Options:

• -t TASK, --task TASK: Provide the task description directly on the command line, skipping the editor.

Applying Patches

kopipasta can apply changes suggested by an LLM directly to your codebase, assuming you are in a Git repository.

1. While running kopipasta in the interactive file selector, press the p key.
2. Paste the entire markdown response from your LLM into the terminal prompt and submit.
3. The tool will find code blocks with file paths (e.g., // FILE: src/main.py) and immediately write those changes to your local files.
4. After applying, use standard Git commands like git diff to review the changes before staging and committing them.

Key Features

• Total Context Control: Interactively select files, directories, snippets, or even individual functions. You see everything that goes into the prompt.
• Interactive Code Patcher: Press p in the file selector to paste and apply LLM-suggested changes directly to your local files. Relies on your version control (like Git) for safety, enabling a fast workflow.
• Transparent & Explicit: No hidden RAG. You know exactly what's in the prompt because you built it. This makes debugging LLM failures possible.
• Web-Aware: Pulls in content directly from URLs—perfect for API documentation.
• Safety First:
  • Automatically respects your .gitignore rules.
  • Detects if you're about to include secrets from a .env file and asks what to do.
• Context-Aware: Keeps a running total of the prompt size (in characters and estimated tokens) so you don't overload the LLM's context window.
• Developer-Friendly:
  • Provides a rich, interactive prompt for writing task descriptions in terminal.
  • Copies the final prompt directly to your clipboard.
  • Provides syntax highlighting during chunk selection.

A Real-World Example

I had a bug where my setup.py didn't include all the dependencies from requirements.txt.

1. I ran kopipasta -t "Update setup.py to read dependencies dynamically from requirements.txt" setup.py requirements.txt.
2. The tool confirmed the inclusion of both files and copied the complete prompt to my clipboard.
3. I pasted the prompt into my LLM chat window.
4. I copied the LLM's response (which included a modified setup.py in a markdown code block).
5. Inside kopipasta, I pressed p, pasted the response, and my local setup.py was updated.
6. I ran git diff to review the changes, then tested and committed.

No manual file reading, no clumsy copy-pasting, just a clean, context-rich prompt that I had full control over, and a seamless way to apply the results.